Link print in the IOU

Aug 6th, 2022
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  3. Use the top toolbar to link print in IOU.
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How to link print in the IOU

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All right, lets get this started! Object Detection plays a vital role in a wide range of applications from Autonomous Driving Systems to Surveillance cameras identifying potential threats. There needs to be a metric to evaluate the quality of bounding boxes after detection. Thats where Intersection over Union or IOU comes into picture. It can be mathematically expressed as Area of Intersection over Area of Union. Lets use this bird image as a reference. We have three object detection models, model A, Model B and model C. Ground Truth has been Illustrated using red color and predictions are shown using cyan color. model A has a relatively higher overlap with the Ground Truth compared to model B. We might conclude that model A is performing better right but hold on, theres more to the story! Model C comes into the picture, it has an even higher overlap with the Ground Truth than model B. In model C we can observe that the localization of prediction is ev

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Intersection over Union formula Get a prediction from your model; Compare the predicted bounding box (or mask) with the ground truth one; Calculate the area of overlap and the area of union between these; Divide the overlap between the annotations by their union; Analyze the obtained value;
The IoU value ranges from 0 to 1, where 0 indicates no overlap between the boxes, and 1 represents complete overlap or identical regions. Higher IoU values indicate greater similarity or agreement between the two bounding boxes.
MeanIoU class Computes the mean Intersection-Over-Union metric. Formula: iou = truepositives / (truepositives + falsepositives + falsenegatives) Intersection-Over-Union is a common evaluation metric for semantic image segmentation.
7. Calculate IoU score: Compute the IoU score for each object in the dataset once we possess the ground truth and predicted bounding boxes. The intersection area of the two bounding boxes is divided by the area of its union to determine the IoU score.
We compute the IoU by dividing the area of the intersection by the area of the union. A higher IoU value indicates a more accurate prediction, while a lower value suggests a poor alignment between the predicted and ground truth bounding boxes.
IOU provides a measure of how well a predicted object aligns with the actual object annotation, enabling the assessment of model accuracy and the fine-tuning of algorithms for improved results. A higher IOU value indicates a better alignment between the predicted and actual regions, reflecting a more accurate model.
Intersection over Union calculation example In such a case: Area of Overlap: 5 * 5 = 25; Area of Union: Area of the Red Bounding Box + Area of the Green Bounding Box - Area of Overlap = 10 10 + 10 10 - 25 = 175. IoU: Area of Overlap / Area of Union = 25 / 175 ~ 0.14 - poor IoU.
Note: 0.5 IoU is typically considered a good score, while 1 is perfect in theory.

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